Import functions and models
load('../Data/GER/ger_list_results_fixed_window.RData')
load('../Data/US/us_list_results_fixed_window.RData')
library(lmtest)
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
library(car)
## Loading required package: carData
library(survival)
library(tidyverse)
## ── Attaching packages ──────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.0 ✓ purrr 0.3.3
## ✓ tibble 3.0.0 ✓ dplyr 0.8.5
## ✓ tidyr 1.0.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0
## Warning: package 'tibble' was built under R version 3.6.2
## ── Conflicts ─────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x dplyr::recode() masks car::recode()
## x purrr::some() masks car::some()
Define Functions
list_iterater <- function(models, test) {
for(i in models){
for(j in i){
if(test == 'qq'){j %>% plot(2)}
if(test == 'ks'){j %>% resid() %>% ks.test(y=pnorm) %>% print()}
if(test == 'bp'){j %>% bptest() %>% print()}
if(test == 'ph'){j %>% cox.zph() %>% print()}
}
}
}
Assumptions GER COVID-19 onsets (proportional hazards)
list_iterater(ger_list_results$ger_cox_prev_onset, test = 'ph')
## chisq df p
## pers 4.52 1 0.034
## GLOBAL 4.52 1 0.034
## chisq df p
## pers 2.76 1 0.09691
## age 14.28 1 0.00016
## male 2.95 1 0.08600
## conservative 8.68 1 0.00322
## GLOBAL 16.67 4 0.00224
## chisq df p
## pers 0.7311 1 0.39
## academics 0.1784 1 0.67
## medinc 0.1109 1 0.74
## manufact 0.0784 1 0.78
## GLOBAL 0.9387 4 0.92
## chisq df p
## pers 5.231 1 0.022
## airport_dist 1.756 1 0.185
## tourism 1.683 1 0.195
## healthcare 0.249 1 0.617
## popdens 3.738 1 0.053
## GLOBAL 9.389 5 0.095
## chisq df p
## pers 1.2095 1 0.271
## age 16.1514 1 5.8e-05
## male 3.8894 1 0.049
## conservative 5.4494 1 0.020
## academics 1.0705 1 0.301
## medinc 1.4807 1 0.224
## manufact 0.1070 1 0.744
## airport_dist 0.6959 1 0.404
## tourism 0.5836 1 0.445
## healthcare 0.0349 1 0.852
## popdens 2.4390 1 0.118
## GLOBAL 23.2077 11 0.017
## chisq df p
## pers 1.33 1 0.25
## GLOBAL 1.33 1 0.25
## chisq df p
## pers 0.0073 1 0.9319
## age 13.8084 1 0.0002
## male 3.3349 1 0.0678
## conservative 7.9125 1 0.0049
## GLOBAL 16.8658 4 0.0021
## chisq df p
## pers 0.0186 1 0.89
## academics 0.1976 1 0.66
## medinc 0.0389 1 0.84
## manufact 0.0418 1 0.84
## GLOBAL 0.2905 4 0.99
## chisq df p
## pers 0.882 1 0.348
## airport_dist 2.401 1 0.121
## tourism 1.541 1 0.214
## healthcare 0.642 1 0.423
## popdens 3.038 1 0.081
## GLOBAL 6.928 5 0.226
## chisq df p
## pers 0.397 1 0.528
## age 17.013 1 3.7e-05
## male 3.788 1 0.052
## conservative 5.717 1 0.017
## academics 1.012 1 0.314
## medinc 1.422 1 0.233
## manufact 0.154 1 0.695
## airport_dist 0.918 1 0.338
## tourism 0.535 1 0.464
## healthcare 0.020 1 0.888
## popdens 2.299 1 0.129
## GLOBAL 23.750 11 0.014
## chisq df p
## pers 14.6 1 0.00013
## GLOBAL 14.6 1 0.00013
## chisq df p
## pers 12.12 1 0.00050
## age 13.67 1 0.00022
## male 2.86 1 0.09069
## conservative 7.37 1 0.00664
## GLOBAL 24.38 4 6.7e-05
## chisq df p
## pers 8.5445 1 0.0035
## academics 0.2101 1 0.6467
## medinc 0.0319 1 0.8582
## manufact 0.0658 1 0.7976
## GLOBAL 8.9481 4 0.0624
## chisq df p
## pers 13.32 1 0.00026
## airport_dist 1.39 1 0.23911
## tourism 1.84 1 0.17483
## healthcare 1.19 1 0.27596
## popdens 2.64 1 0.10430
## GLOBAL 17.47 5 0.00370
## chisq df p
## pers 9.22198 1 0.0024
## age 15.77702 1 7.1e-05
## male 3.84206 1 0.0500
## conservative 5.13675 1 0.0234
## academics 1.01781 1 0.3130
## medinc 1.77555 1 0.1827
## manufact 0.09080 1 0.7632
## airport_dist 0.62927 1 0.4276
## tourism 0.60674 1 0.4360
## healthcare 0.00677 1 0.9344
## popdens 2.35550 1 0.1248
## GLOBAL 28.20277 11 0.0030
## chisq df p
## pers 7.06 1 0.0079
## GLOBAL 7.06 1 0.0079
## chisq df p
## pers 4.24 1 0.03957
## age 13.76 1 0.00021
## male 2.99 1 0.08388
## conservative 7.79 1 0.00527
## GLOBAL 20.59 4 0.00038
## chisq df p
## pers 2.8541 1 0.091
## academics 0.0515 1 0.821
## medinc 0.1330 1 0.715
## manufact 0.0353 1 0.851
## GLOBAL 3.2582 4 0.516
## chisq df p
## pers 7.26 1 0.0071
## airport_dist 1.76 1 0.1840
## tourism 1.76 1 0.1842
## healthcare 0.69 1 0.4061
## popdens 2.84 1 0.0921
## GLOBAL 12.70 5 0.0264
## chisq df p
## pers 2.9407 1 0.0864
## age 15.9577 1 6.5e-05
## male 3.9656 1 0.0464
## conservative 5.3042 1 0.0213
## academics 0.8371 1 0.3602
## medinc 1.4335 1 0.2312
## manufact 0.1499 1 0.6986
## airport_dist 0.6402 1 0.4236
## tourism 0.5696 1 0.4504
## healthcare 0.0299 1 0.8627
## popdens 2.3287 1 0.1270
## GLOBAL 26.9323 11 0.0047
## chisq df p
## pers 13.8 1 2e-04
## GLOBAL 13.8 1 2e-04
## chisq df p
## pers 9.71 1 0.00183
## age 12.19 1 0.00048
## male 2.53 1 0.11158
## conservative 7.65 1 0.00569
## GLOBAL 24.19 4 7.3e-05
## chisq df p
## pers 2.5946 1 0.11
## academics 0.0986 1 0.75
## medinc 0.4625 1 0.50
## manufact 0.1113 1 0.74
## GLOBAL 3.4193 4 0.49
## chisq df p
## pers 13.83 1 0.0002
## airport_dist 1.63 1 0.2013
## tourism 1.69 1 0.1939
## healthcare 0.43 1 0.5120
## popdens 3.70 1 0.0543
## GLOBAL 17.72 5 0.0033
## chisq df p
## pers 2.26904 1 0.13198
## age 14.32973 1 0.00015
## male 3.46706 1 0.06260
## conservative 5.06956 1 0.02435
## academics 0.92726 1 0.33558
## medinc 0.68421 1 0.40814
## manufact 0.00395 1 0.94989
## airport_dist 0.59959 1 0.43873
## tourism 0.51675 1 0.47223
## healthcare 0.08204 1 0.77455
## popdens 2.72761 1 0.09863
## GLOBAL 23.50172 11 0.01501
Assumptions US COVID-19 onsets (proportional hazards)
list_iterater(us_list_results$us_cox_prev_onset, test = 'ph')
## chisq df p
## pers 61.6 1 4.2e-15
## GLOBAL 61.6 1 4.2e-15
## chisq df p
## pers 40.6084 1 1.9e-10
## age 0.0204 1 0.8866
## male 10.7162 1 0.0011
## conservative 75.4477 1 < 2e-16
## GLOBAL 96.5136 4 < 2e-16
## chisq df p
## pers 40.3 1 2.2e-10
## academics 107.9 1 < 2e-16
## medinc 47.8 1 4.7e-12
## manufact 55.1 1 1.1e-13
## GLOBAL 123.7 4 < 2e-16
## chisq df p
## pers 42.234 1 8.1e-11
## airport_dist 0.916 1 0.33846
## tourism 6.455 1 0.01106
## healthcare 49.690 1 1.8e-12
## popdens 13.509 1 0.00024
## GLOBAL 73.998 5 1.5e-14
## chisq df p
## pers 3.15e+01 1 2.0e-08
## age 7.94e-04 1 0.97752
## male 1.17e+01 1 0.00063
## conservative 7.40e+01 1 < 2e-16
## academics 9.68e+01 1 < 2e-16
## medinc 4.17e+01 1 1.1e-10
## manufact 4.46e+01 1 2.4e-11
## airport_dist 5.13e-03 1 0.94290
## tourism 6.10e+00 1 0.01353
## healthcare 4.91e+01 1 2.4e-12
## popdens 3.72e+01 1 1.1e-09
## GLOBAL 1.43e+02 11 < 2e-16
## chisq df p
## pers 6.57 1 0.01
## GLOBAL 6.57 1 0.01
## chisq df p
## pers 10.597 1 0.00113
## age 0.322 1 0.57065
## male 10.951 1 0.00094
## conservative 86.523 1 < 2e-16
## GLOBAL 107.274 4 < 2e-16
## chisq df p
## pers 3.85 1 0.05
## academics 115.52 1 < 2e-16
## medinc 52.02 1 5.5e-13
## manufact 56.96 1 4.5e-14
## GLOBAL 128.68 4 < 2e-16
## chisq df p
## pers 7.462 1 0.00630
## airport_dist 0.856 1 0.35481
## tourism 6.983 1 0.00823
## healthcare 53.282 1 2.9e-13
## popdens 13.292 1 0.00027
## GLOBAL 58.576 5 2.4e-11
## chisq df p
## pers 6.68e+00 1 0.0097
## age 1.48e-04 1 0.9903
## male 1.05e+01 1 0.0012
## conservative 7.81e+01 1 < 2e-16
## academics 1.01e+02 1 < 2e-16
## medinc 4.54e+01 1 1.6e-11
## manufact 4.55e+01 1 1.5e-11
## airport_dist 9.26e-02 1 0.7609
## tourism 6.47e+00 1 0.0110
## healthcare 5.33e+01 1 2.9e-13
## popdens 3.46e+01 1 4.0e-09
## GLOBAL 1.52e+02 11 < 2e-16
## chisq df p
## pers 2.74 1 0.098
## GLOBAL 2.74 1 0.098
## chisq df p
## pers 1.953 1 0.1623
## age 0.322 1 0.5704
## male 12.545 1 0.0004
## conservative 89.021 1 <2e-16
## GLOBAL 95.248 4 <2e-16
## chisq df p
## pers 1.91 1 0.17
## academics 114.78 1 < 2e-16
## medinc 53.16 1 3.1e-13
## manufact 58.62 1 1.9e-14
## GLOBAL 130.14 4 < 2e-16
## chisq df p
## pers 1.56 1 0.21152
## airport_dist 1.09 1 0.29679
## tourism 6.49 1 0.01086
## healthcare 55.32 1 1e-13
## popdens 14.63 1 0.00013
## GLOBAL 58.99 5 2e-11
## chisq df p
## pers 1.0607 1 0.30305
## age 0.0267 1 0.87009
## male 12.7667 1 0.00035
## conservative 79.8980 1 < 2e-16
## academics 100.7565 1 < 2e-16
## medinc 44.6123 1 2.4e-11
## manufact 46.3450 1 9.9e-12
## airport_dist 0.0107 1 0.91751
## tourism 6.1069 1 0.01347
## healthcare 52.4435 1 4.4e-13
## popdens 38.5603 1 5.3e-10
## GLOBAL 152.4888 11 < 2e-16
## chisq df p
## pers 2.83 1 0.092
## GLOBAL 2.83 1 0.092
## chisq df p
## pers 4.677 1 0.03056
## age 0.373 1 0.54131
## male 11.923 1 0.00055
## conservative 88.977 1 < 2e-16
## GLOBAL 109.853 4 < 2e-16
## chisq df p
## pers 0.671 1 0.41
## academics 116.350 1 < 2e-16
## medinc 53.619 1 2.4e-13
## manufact 61.091 1 5.5e-15
## GLOBAL 132.575 4 < 2e-16
## chisq df p
## pers 1.86 1 0.1729
## airport_dist 1.20 1 0.2726
## tourism 6.72 1 0.0096
## healthcare 56.55 1 5.5e-14
## popdens 16.63 1 4.5e-05
## GLOBAL 59.33 5 1.7e-11
## chisq df p
## pers 1.47e+00 1 0.22600
## age 2.97e-02 1 0.86308
## male 1.16e+01 1 0.00066
## conservative 8.27e+01 1 < 2e-16
## academics 1.04e+02 1 < 2e-16
## medinc 4.57e+01 1 1.4e-11
## manufact 4.82e+01 1 3.8e-12
## airport_dist 6.07e-03 1 0.93791
## tourism 6.20e+00 1 0.01274
## healthcare 5.53e+01 1 1.0e-13
## popdens 4.17e+01 1 1.1e-10
## GLOBAL 1.56e+02 11 < 2e-16
## chisq df p
## pers 28.6 1 8.7e-08
## GLOBAL 28.6 1 8.7e-08
## chisq df p
## pers 16.1841 1 5.7e-05
## age 0.0996 1 0.752
## male 10.8343 1 0.001
## conservative 86.3413 1 < 2e-16
## GLOBAL 95.2857 4 < 2e-16
## chisq df p
## pers 25.7 1 4.0e-07
## academics 120.1 1 < 2e-16
## medinc 56.3 1 6.2e-14
## manufact 56.0 1 7.4e-14
## GLOBAL 135.3 4 < 2e-16
## chisq df p
## pers 19.913 1 8.1e-06
## airport_dist 0.332 1 0.56452
## tourism 5.085 1 0.02414
## healthcare 57.477 1 3.4e-14
## popdens 12.933 1 0.00032
## GLOBAL 74.240 5 1.3e-14
## chisq df p
## pers 1.44e+01 1 0.00015
## age 4.65e-03 1 0.94564
## male 1.13e+01 1 0.00078
## conservative 7.77e+01 1 < 2e-16
## academics 1.04e+02 1 < 2e-16
## medinc 4.79e+01 1 4.4e-12
## manufact 4.48e+01 1 2.2e-11
## airport_dist 6.10e-02 1 0.80493
## tourism 5.28e+00 1 0.02151
## healthcare 5.43e+01 1 1.7e-13
## popdens 3.43e+01 1 4.8e-09
## GLOBAL 1.56e+02 11 < 2e-16
Assumptions GER COVID-19 growth rates (normality of residuals)
list_iterater(ger_list_results$ger_lm_prev_slope, test = 'qq')
list_iterater(ger_list_results$ger_lm_prev_slope, test = 'bp')
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 0.10128, df = 1, p-value = 0.7503
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 1.9618, df = 4, p-value = 0.7428
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 14.782, df = 4, p-value = 0.005176
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 9.2165, df = 5, p-value = 0.1007
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 24.657, df = 11, p-value = 0.01023
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 0.40351, df = 1, p-value = 0.5253
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 2.0729, df = 4, p-value = 0.7223
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 14.461, df = 4, p-value = 0.005961
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 9.9498, df = 5, p-value = 0.07667
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 24.132, df = 11, p-value = 0.01219
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 0.013425, df = 1, p-value = 0.9078
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 1.9958, df = 4, p-value = 0.7365
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 12.444, df = 4, p-value = 0.01434
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 9.7153, df = 5, p-value = 0.08372
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 24.05, df = 11, p-value = 0.01253
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 2.0338, df = 1, p-value = 0.1538
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 1.7397, df = 4, p-value = 0.7835
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 13.801, df = 4, p-value = 0.007958
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 16.165, df = 5, p-value = 0.006389
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 24.197, df = 11, p-value = 0.01193
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 0.0091219, df = 1, p-value = 0.9239
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 2.281, df = 4, p-value = 0.6842
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 13.807, df = 4, p-value = 0.007938
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 12.565, df = 5, p-value = 0.02782
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 25.207, df = 11, p-value = 0.008503
Assumptions US COVID-19 growth rates (normality of residuals)
list_iterater(us_list_results$us_lm_prev_slope, test = 'qq')
list_iterater(us_list_results$us_lm_prev_slope, test = 'bp')
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 27.212, df = 1, p-value = 1.823e-07
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 61.721, df = 4, p-value = 1.261e-12
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 28.513, df = 4, p-value = 9.818e-06
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 91.398, df = 5, p-value < 2.2e-16
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 124.17, df = 11, p-value < 2.2e-16
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 0.56555, df = 1, p-value = 0.452
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 61.782, df = 4, p-value = 1.224e-12
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 37.796, df = 4, p-value = 1.235e-07
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 98.721, df = 5, p-value < 2.2e-16
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 132.44, df = 11, p-value < 2.2e-16
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 3.709, df = 1, p-value = 0.05412
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 62.437, df = 4, p-value = 8.914e-13
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 21.864, df = 4, p-value = 0.0002133
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 88.004, df = 5, p-value < 2.2e-16
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 122.68, df = 11, p-value < 2.2e-16
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 7.4312, df = 1, p-value = 0.00641
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 68.758, df = 4, p-value = 4.15e-14
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 60.223, df = 4, p-value = 2.604e-12
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 115.14, df = 5, p-value < 2.2e-16
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 153.09, df = 11, p-value < 2.2e-16
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 13.367, df = 1, p-value = 0.000256
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 61.101, df = 4, p-value = 1.702e-12
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 33.401, df = 4, p-value = 9.887e-07
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 97.548, df = 5, p-value < 2.2e-16
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 127.31, df = 11, p-value < 2.2e-16
Assumptions GER socdist onsets
list_iterater(ger_list_results$ger_cox_socdist_cpt, test = 'ph')
## chisq df p
## pers 0.404 1 0.52
## GLOBAL 0.404 1 0.52
## chisq df p
## pers 1.154 1 0.2827
## age 1.266 1 0.2604
## male 7.065 1 0.0079
## conservative 0.874 1 0.3499
## GLOBAL 9.658 4 0.0466
## chisq df p
## pers 0.2426 1 0.622
## academics 0.0241 1 0.877
## medinc 4.6830 1 0.030
## manufact 4.8907 1 0.027
## GLOBAL 7.6983 4 0.103
## chisq df p
## pers 0.21564 1 0.642
## airport_dist 2.72244 1 0.099
## tourism 0.00569 1 0.940
## healthcare 1.45086 1 0.228
## popdens 0.01079 1 0.917
## GLOBAL 4.20382 5 0.520
## chisq df p
## pers 0.46887 1 0.4935
## age 3.34315 1 0.0675
## male 7.50922 1 0.0061
## conservative 2.19579 1 0.1384
## academics 0.00651 1 0.9357
## medinc 3.57194 1 0.0588
## manufact 2.06999 1 0.1502
## airport_dist 5.44624 1 0.0196
## tourism 0.52194 1 0.4700
## healthcare 2.58597 1 0.1078
## popdens 0.10598 1 0.7448
## onset_prev 3.34065 1 0.0676
## slope_prev 4.71555 1 0.0299
## GLOBAL 16.82209 13 0.2076
## chisq df p
## pers 0.159 1 0.69
## GLOBAL 0.159 1 0.69
## chisq df p
## pers 0.854 1 0.3554
## age 1.073 1 0.3003
## male 6.922 1 0.0085
## conservative 0.676 1 0.4108
## GLOBAL 8.157 4 0.0860
## chisq df p
## pers 0.3156 1 0.574
## academics 0.0588 1 0.808
## medinc 4.0703 1 0.044
## manufact 5.0115 1 0.025
## GLOBAL 6.3765 4 0.173
## chisq df p
## pers 0.03622 1 0.849
## airport_dist 2.93476 1 0.087
## tourism 0.00399 1 0.950
## healthcare 1.88139 1 0.170
## popdens 0.07296 1 0.787
## GLOBAL 5.30803 5 0.379
## chisq df p
## pers 0.31735 1 0.573
## age 3.26441 1 0.071
## male 7.27168 1 0.007
## conservative 2.12924 1 0.145
## academics 0.00213 1 0.963
## medinc 3.49245 1 0.062
## manufact 2.05690 1 0.152
## airport_dist 5.80195 1 0.016
## tourism 0.46195 1 0.497
## healthcare 2.80165 1 0.094
## popdens 0.04351 1 0.835
## onset_prev 3.42436 1 0.064
## slope_prev 4.75551 1 0.029
## GLOBAL 16.22651 13 0.237
## chisq df p
## pers 0.154 1 0.7
## GLOBAL 0.154 1 0.7
## chisq df p
## pers 0.404 1 0.5249
## age 0.836 1 0.3604
## male 6.729 1 0.0095
## conservative 0.550 1 0.4583
## GLOBAL 7.606 4 0.1071
## chisq df p
## pers 0.0844 1 0.771
## academics 0.0204 1 0.886
## medinc 3.9203 1 0.048
## manufact 4.0666 1 0.044
## GLOBAL 5.8512 4 0.211
## chisq df p
## pers 0.1585 1 0.69
## airport_dist 2.6285 1 0.10
## tourism 0.0106 1 0.92
## healthcare 1.4996 1 0.22
## popdens 0.0151 1 0.90
## GLOBAL 4.1465 5 0.53
## chisq df p
## pers 0.0442 1 0.8334
## age 2.9245 1 0.0872
## male 7.3914 1 0.0066
## conservative 2.0411 1 0.1531
## academics 0.0117 1 0.9139
## medinc 2.5679 1 0.1091
## manufact 1.4444 1 0.2294
## airport_dist 5.7027 1 0.0169
## tourism 0.6132 1 0.4336
## healthcare 2.9742 1 0.0846
## popdens 0.0553 1 0.8142
## onset_prev 2.7730 1 0.0959
## slope_prev 4.2054 1 0.0403
## GLOBAL 16.9939 13 0.1996
## chisq df p
## pers 0.134 1 0.71
## GLOBAL 0.134 1 0.71
## chisq df p
## pers 0.584 1 0.445
## age 1.041 1 0.308
## male 6.496 1 0.011
## conservative 0.690 1 0.406
## GLOBAL 7.954 4 0.093
## chisq df p
## pers 0.233 1 0.63
## academics 0.024 1 0.88
## medinc 4.215 1 0.04
## manufact 4.696 1 0.03
## GLOBAL 6.208 4 0.18
## chisq df p
## pers 0.04716 1 0.828
## airport_dist 2.93708 1 0.087
## tourism 0.00592 1 0.939
## healthcare 1.45116 1 0.228
## popdens 0.01290 1 0.910
## GLOBAL 4.78550 5 0.443
## chisq df p
## pers 6.07e-01 1 0.4358
## age 3.25e+00 1 0.0712
## male 7.14e+00 1 0.0076
## conservative 2.10e+00 1 0.1477
## academics 7.84e-04 1 0.9777
## medinc 3.48e+00 1 0.0622
## manufact 1.99e+00 1 0.1580
## airport_dist 5.89e+00 1 0.0152
## tourism 4.97e-01 1 0.4807
## healthcare 2.62e+00 1 0.1053
## popdens 9.50e-02 1 0.7579
## onset_prev 3.32e+00 1 0.0684
## slope_prev 4.81e+00 1 0.0283
## GLOBAL 1.64e+01 13 0.2283
## chisq df p
## pers 0.944 1 0.33
## GLOBAL 0.944 1 0.33
## chisq df p
## pers 0.264 1 0.607
## age 1.201 1 0.273
## male 6.375 1 0.012
## conservative 0.843 1 0.359
## GLOBAL 7.168 4 0.127
## chisq df p
## pers 1.1126 1 0.292
## academics 0.0172 1 0.896
## medinc 4.8148 1 0.028
## manufact 4.9173 1 0.027
## GLOBAL 6.7084 4 0.152
## chisq df p
## pers 1.024360 1 0.311
## airport_dist 2.746720 1 0.097
## tourism 0.001333 1 0.971
## healthcare 1.290102 1 0.256
## popdens 0.000404 1 0.984
## GLOBAL 4.572367 5 0.470
## chisq df p
## pers 1.15693 1 0.2821
## age 3.28630 1 0.0699
## male 6.96337 1 0.0083
## conservative 2.23667 1 0.1348
## academics 0.00162 1 0.9679
## medinc 3.58681 1 0.0582
## manufact 2.04020 1 0.1532
## airport_dist 5.71785 1 0.0168
## tourism 0.43810 1 0.5080
## healthcare 2.43042 1 0.1190
## popdens 0.14832 1 0.7001
## onset_prev 3.43784 1 0.0637
## slope_prev 4.91867 1 0.0266
## GLOBAL 16.62267 13 0.2171
Assumptions US socdist onsets
list_iterater(us_list_results$us_cox_socdist_cpt, test = 'ph')
## chisq df p
## pers 39.7 1 3e-10
## GLOBAL 39.7 1 3e-10
## chisq df p
## pers 39.94 1 2.6e-10
## age 3.29 1 0.06951
## male 8.97 1 0.00274
## conservative 11.75 1 0.00061
## GLOBAL 46.79 4 1.7e-09
## chisq df p
## pers 35.253 1 2.9e-09
## academics 11.100 1 0.00086
## medinc 0.688 1 0.40675
## manufact 6.541 1 0.01054
## GLOBAL 37.875 4 1.2e-07
## chisq df p
## pers 46.23 1 1.0e-11
## airport_dist 31.52 1 2.0e-08
## tourism 26.81 1 2.2e-07
## healthcare 2.83 1 0.092
## popdens 57.82 1 2.9e-14
## GLOBAL 112.47 5 < 2e-16
## chisq df p
## pers 37.40 1 9.6e-10
## age 2.30 1 0.12952
## male 7.56 1 0.00596
## conservative 18.52 1 1.7e-05
## academics 14.15 1 0.00017
## medinc 1.62 1 0.20320
## manufact 6.91 1 0.00856
## airport_dist 33.36 1 7.6e-09
## tourism 21.82 1 3.0e-06
## healthcare 2.09 1 0.14845
## popdens 39.28 1 3.7e-10
## onset_prev 73.00 1 < 2e-16
## slope_prev 66.21 1 4.1e-16
## GLOBAL 136.53 13 < 2e-16
## chisq df p
## pers 12.3 1 0.00045
## GLOBAL 12.3 1 0.00045
## chisq df p
## pers 12.01 1 0.00053
## age 2.43 1 0.11932
## male 6.91 1 0.00858
## conservative 9.74 1 0.00180
## GLOBAL 26.25 4 2.8e-05
## chisq df p
## pers 9.834 1 0.0017
## academics 8.571 1 0.0034
## medinc 0.208 1 0.6481
## manufact 5.842 1 0.0157
## GLOBAL 26.730 4 2.3e-05
## chisq df p
## pers 8.88 1 0.0029
## airport_dist 33.80 1 6.1e-09
## tourism 25.76 1 3.9e-07
## healthcare 1.56 1 0.2116
## popdens 72.97 1 < 2e-16
## GLOBAL 123.12 5 < 2e-16
## chisq df p
## pers 8.861 1 0.00291
## age 1.502 1 0.22029
## male 6.349 1 0.01174
## conservative 17.152 1 3.5e-05
## academics 12.346 1 0.00044
## medinc 0.969 1 0.32498
## manufact 7.453 1 0.00633
## airport_dist 34.721 1 3.8e-09
## tourism 21.579 1 3.4e-06
## healthcare 1.307 1 0.25289
## popdens 40.702 1 1.8e-10
## onset_prev 69.578 1 < 2e-16
## slope_prev 66.958 1 2.8e-16
## GLOBAL 134.867 13 < 2e-16
## chisq df p
## pers 0.487 1 0.49
## GLOBAL 0.487 1 0.49
## chisq df p
## pers 0.36 1 0.54853
## age 3.07 1 0.07955
## male 7.68 1 0.00557
## conservative 9.81 1 0.00174
## GLOBAL 18.68 4 0.00091
## chisq df p
## pers 0.0724 1 0.7879
## academics 10.0939 1 0.0015
## medinc 0.5353 1 0.4644
## manufact 6.6588 1 0.0099
## GLOBAL 17.7158 4 0.0014
## chisq df p
## pers 0.40 1 0.53
## airport_dist 30.10 1 4.1e-08
## tourism 27.49 1 1.6e-07
## healthcare 2.17 1 0.14
## popdens 70.44 1 < 2e-16
## GLOBAL 114.32 5 < 2e-16
## chisq df p
## pers 7.55e-04 1 0.97807
## age 1.93e+00 1 0.16431
## male 7.00e+00 1 0.00813
## conservative 1.72e+01 1 3.3e-05
## academics 1.40e+01 1 0.00019
## medinc 1.54e+00 1 0.21466
## manufact 7.44e+00 1 0.00639
## airport_dist 3.30e+01 1 9.2e-09
## tourism 2.21e+01 1 2.5e-06
## healthcare 1.73e+00 1 0.18873
## popdens 4.08e+01 1 1.6e-10
## onset_prev 7.26e+01 1 < 2e-16
## slope_prev 6.74e+01 1 < 2e-16
## GLOBAL 1.33e+02 13 < 2e-16
## chisq df p
## pers 16 1 6.2e-05
## GLOBAL 16 1 6.2e-05
## chisq df p
## pers 15.65 1 7.6e-05
## age 2.94 1 0.0867
## male 7.49 1 0.0062
## conservative 9.79 1 0.0018
## GLOBAL 28.39 4 1.0e-05
## chisq df p
## pers 13.423 1 0.00025
## academics 9.485 1 0.00207
## medinc 0.395 1 0.52966
## manufact 6.174 1 0.01296
## GLOBAL 32.320 4 1.6e-06
## chisq df p
## pers 10.82 1 0.001
## airport_dist 31.89 1 1.6e-08
## tourism 25.77 1 3.8e-07
## healthcare 1.76 1 0.184
## popdens 71.58 1 < 2e-16
## GLOBAL 127.26 5 < 2e-16
## chisq df p
## pers 12.51 1 0.00041
## age 1.80 1 0.17965
## male 6.80 1 0.00911
## conservative 17.11 1 3.5e-05
## academics 13.40 1 0.00025
## medinc 1.29 1 0.25537
## manufact 7.58 1 0.00591
## airport_dist 33.35 1 7.7e-09
## tourism 21.56 1 3.4e-06
## healthcare 1.47 1 0.22469
## popdens 40.61 1 1.9e-10
## onset_prev 72.23 1 < 2e-16
## slope_prev 68.35 1 < 2e-16
## GLOBAL 137.68 13 < 2e-16
## chisq df p
## pers 8.6 1 0.0034
## GLOBAL 8.6 1 0.0034
## chisq df p
## pers 8.88 1 0.00289
## age 2.07 1 0.14999
## male 7.06 1 0.00788
## conservative 9.73 1 0.00181
## GLOBAL 19.75 4 0.00056
## chisq df p
## pers 9.806 1 0.00174
## academics 7.980 1 0.00473
## medinc 0.118 1 0.73103
## manufact 7.012 1 0.00810
## GLOBAL 20.852 4 0.00034
## chisq df p
## pers 8.36 1 0.0038
## airport_dist 32.79 1 1.0e-08
## tourism 25.97 1 3.5e-07
## healthcare 1.35 1 0.2453
## popdens 62.20 1 3.1e-15
## GLOBAL 108.93 5 < 2e-16
## chisq df p
## pers 11.725 1 0.00062
## age 1.449 1 0.22864
## male 6.709 1 0.00959
## conservative 18.242 1 1.9e-05
## academics 12.639 1 0.00038
## medinc 0.918 1 0.33792
## manufact 8.296 1 0.00397
## airport_dist 34.192 1 5.0e-09
## tourism 22.255 1 2.4e-06
## healthcare 1.310 1 0.25232
## popdens 40.266 1 2.2e-10
## onset_prev 72.228 1 < 2e-16
## slope_prev 68.799 1 < 2e-16
## GLOBAL 138.349 13 < 2e-16
Assumptions GER socdist adjustment levels
list_iterater(ger_list_results$ger_lm_socdist_mean, test = 'qq')
list_iterater(ger_list_results$ger_lm_socdist_mean, test = 'bp')
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 4.1158, df = 1, p-value = 0.04248
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 8.8268, df = 4, p-value = 0.06558
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 37.09, df = 4, p-value = 1.726e-07
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 1.2643, df = 5, p-value = 0.9386
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 25.778, df = 13, p-value = 0.0182
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 1.8021, df = 1, p-value = 0.1795
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 15.008, df = 4, p-value = 0.004684
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 42.745, df = 4, p-value = 1.169e-08
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 8.1449, df = 5, p-value = 0.1484
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 31.748, df = 13, p-value = 0.002618
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 2.549, df = 1, p-value = 0.1104
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 12.838, df = 4, p-value = 0.0121
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 37.38, df = 4, p-value = 1.504e-07
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 4.6026, df = 5, p-value = 0.4663
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 26.394, df = 13, p-value = 0.01504
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 7.4375, df = 1, p-value = 0.006388
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 14.601, df = 4, p-value = 0.005605
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 41.909, df = 4, p-value = 1.742e-08
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 8.6427, df = 5, p-value = 0.1242
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 34.447, df = 13, p-value = 0.001029
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 2.0602, df = 1, p-value = 0.1512
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 13.623, df = 4, p-value = 0.0086
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 37.353, df = 4, p-value = 1.524e-07
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 7.1669, df = 5, p-value = 0.2085
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 27.611, df = 13, p-value = 0.01025
Assumptions US socdist adjustment levels
list_iterater(us_list_results$us_lm_socdist_mean, test = 'qq')
list_iterater(us_list_results$us_lm_socdist_mean, test = 'bp')
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 11.76, df = 1, p-value = 0.0006051
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 11.57, df = 4, p-value = 0.02085
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 9.5642, df = 4, p-value = 0.04844
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 27.495, df = 5, p-value = 4.568e-05
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 27.97, df = 13, p-value = 0.009139
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 6.665, df = 1, p-value = 0.009832
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 10.04, df = 4, p-value = 0.03976
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 11.371, df = 4, p-value = 0.0227
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 34.42, df = 5, p-value = 1.964e-06
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 28.308, df = 13, p-value = 0.008195
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 0.18017, df = 1, p-value = 0.6712
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 14.354, df = 4, p-value = 0.006248
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 10.368, df = 4, p-value = 0.03467
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 34.862, df = 5, p-value = 1.603e-06
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 28.459, df = 13, p-value = 0.007804
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 14.473, df = 1, p-value = 0.0001422
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 11.976, df = 4, p-value = 0.01753
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 15.041, df = 4, p-value = 0.004617
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 38.478, df = 5, p-value = 3.024e-07
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 29.336, df = 13, p-value = 0.005863
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 9.3425, df = 1, p-value = 0.002239
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 20.042, df = 4, p-value = 0.0004899
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 11.506, df = 4, p-value = 0.02143
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 36.513, df = 5, p-value = 7.498e-07
##
##
## studentized Breusch-Pagan test
##
## data: .
## BP = 31.348, df = 13, p-value = 0.002998